Method and apparatus for estimating state of battery

ABSTRACT

A battery state estimation method and apparatus are provided. Sensing data of a battery is received, and feature information is acquired by preprocessing the sensing data. The preprocessed sensing data is selected based on the feature information, and state information of the battery is determined based on at least one of the selected preprocessed sensing data or previous state information of the battery.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.15/864,252 filed on Jan. 8, 2018 which claims the benefit under 35 USC §119(a) of Korean Patent Application No. 10-2017-0007869, filed on Jan.17, 2017, in the Korean Intellectual Property Office, the entiredisclosure of which is incorporated herein by reference for allpurposes.

BACKGROUND 1. Field

The following description relates to a method and apparatus forestimating a state of a battery.

2. Description of Related Art

Many electronic devices include a rechargeable battery. To manage therechargeable battery, the electronic device may include a batterymanagement system (BMS). The BMS estimates a battery state, for example,a state of charge (SOC) or a state of health (SOH), to optimize theoperation of the electronic device or the battery. The availability ofthe battery is extended based on an accuracy of estimation of the SOCand SOH, and thus, it is important that the BMS accurately estimates thebattery state.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect there is provided, a battery state estimationmethod including receiving sensing data of a battery, acquiring featureinformation by preprocessing the sensing data, selecting thepreprocessed sensing data based on the feature information, anddetermining state information of the battery based on either one or bothof the selected preprocessed sensing data and previous state informationof the battery.

The feature information may include any one or any combination of a datasize of the preprocessed sensing data and a variance value of thepreprocessed sensing data.

The selecting of the preprocessed sensing data may include calculating areliability of the preprocessed sensing data based on the featureinformation, and selecting the preprocessed sensing data by comparingthe reliability to a threshold.

The selection of the preprocessed sensing data may include selecting thepreprocessed sensing data in response to a length of the preprocessedsensing data being greater than or equal to a threshold.

The selection of the preprocessed sensing data may include calculating adeviation between each of sample values of the preprocessed sensing dataand a previous sample value of the each of the sample values, andselecting the preprocessed sensing data based on a variance of thecalculated deviation.

The determining of the state information may include determining thestate information based on a previous state information in response tothe preprocessed sensing data not being selected.

The determining of the state information may include determining theprevious state information as the state information in response to thepreprocessed sensing data not being selected.

The determining of the state information may include determining thestate information based on the preprocessed sensing data in response tothe preprocessed sensing data being selected.

The acquiring of the feature information may include filtering thesensing data, and downsampling the filtered sensing data.

The preprocessed sensing data may correspond to data suitable forestimation of a state of the battery, in response to the preprocessedsensing data being selected, and the preprocessed sensing data maycorrespond to data unsuitable for the estimation of the state of thebattery, in response to the preprocessed sensing data not beingselected.

In another general aspect there is provided, a battery state estimationapparatus including a controller configured to receive sensing data of abattery, to acquire feature information by preprocessing the sensingdata, select the preprocessed sensing based on the feature information,and to determine state information of the battery based on either one orboth of the selected preprocessed sensing data and previous stateinformation of the battery.

The feature information may include any one or any combination of a datasize of the preprocessed sensing data and a variance value of thepreprocessed sensing data.

The controller may be configured to calculate a reliability of thepreprocessed sensing data based on the feature information and to selectthe preprocessed sensing data by comparing the reliability to athreshold.

The controller may be configured to select the preprocessed sensing datain response to a length of the preprocessed sensing data being greaterthan or equal to a threshold.

The controller may be configured to calculate a deviation between eachof sample values of the preprocessed sensing data and a previous samplevalue of the each of the sample values, and to select the preprocessedsensing data based on a variance of the calculated deviation.

The controller may be configured to determine the state informationbased on a previous state information in response to the preprocessedsensing data not being selected.

The controller may be configured to determine the previous stateinformation as the state information in response to the preprocessedsensing data not being selected.

The controller may be configured to determine the state informationbased on the preprocessed sensing data in response to the preprocessedsensing data being selected.

The controller may be configured to filter the sensing data and todownsample the filtered sensing data.

The preprocessed sensing data may correspond to data suitable forestimation of a state of the battery, in response to the preprocessedsensing data being selected, and the preprocessed sensing data maycorrespond to data unsuitable for the estimation of the state of thebattery, in response to the preprocessed sensing data not beingselected.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a battery stateestimation apparatus.

FIG. 2 is a diagram illustrating an example of a distribution ofestimation errors for each data length.

FIGS. 3 and 4 are diagrams illustrating examples of statisticalinformation of sensing data.

FIGS. 5 and 6 are diagrams illustrating examples of estimation of astate of a battery when sensing data is unsuitable for the estimation ofthe state.

FIG. 7 is a diagram illustrating an example of a battery stateestimation result.

FIGS. 8 and 9 are diagrams illustrating examples of a battery managementsystem (BMS) with a master-slave structure.

FIG. 10 is a diagram illustrating an example of a battery stateestimation method.

FIGS. 11 and 12 are diagrams illustrating examples of vehicles.

Throughout the drawings and the detailed description, unless otherwisedescribed or provided, the same drawing reference numerals will beunderstood to refer to the same elements, features, and structures. Thedrawings may not be to scale, and the relative size, proportions, anddepiction of elements in the drawings may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after gaining a thoroughunderstanding of the disclosure of this application. For example, thesequences of operations described herein are merely examples, and arenot limited to those set forth herein, but may be changed as will beapparent after an understanding of the disclosure of this application,with the exception of operations necessarily occurring in a certainorder. Also, descriptions of functions and constructions that are knownin the art may be omitted for increased clarity and conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or systems described herein that will beapparent after an understanding of the disclosure of this application.

Various modifications may be made to examples. However, it should beunderstood that these examples are not construed as limited to theillustrated forms and include all changes, equivalents or alternativeswithin the idea and the technical scope of this disclosure.

The terminology used herein is for the purpose of describing particularexamples only and is not intended to be limiting of examples. As usedherein, the singular forms are intended to include the plural forms aswell, unless the context clearly indicates otherwise.

Regarding the reference numerals assigned to the elements in thedrawings, it should be noted that the same elements will be designatedby the same reference numerals, wherever possible, even though they areshown in different drawings. Also, in describing of examples, detaileddescription of well-known related structures or functions will beomitted when it is deemed that such description will cause ambiguousinterpretation of the present disclosure.

FIG. 1 illustrates an example of a battery state estimation apparatus100.

Referring to FIG. 1, the battery state estimation apparatus 100 includesa receiver 110, a preprocessor 120, a selector 130, and a stateestimator 140.

The receiver 110, the preprocessor 120, the selector 130, and the stateestimator 140 are implemented by, for example, at least one processingdevice (for example, a processor or a controller).

The receiver 110 receives sensing data from a battery, such as, forexample, a battery cell, a battery module, or a battery pack.

The receiver 110 receives sensing data of a battery from at least onesensor. In an example, a voltage sensor generates voltage data of abattery by sensing a voltage of the battery at preset intervals (forexample, at each time interval of 1 second) and transmits the generatedvoltage data to the battery state estimation apparatus 100. The receiver110 receives the voltage data from the voltage sensor. In anotherexample, a current sensor generates current data of a battery by sensinga current of the battery at preset intervals (for example, at each timeinterval of 1 second) and transmits the generated current data to thebattery state estimation apparatus 100. The receiver 110 receives thecurrent data from the current sensor. The voltage sensor and the currentsensor are merely examples of the sensor, and other sensors may be usedwithout departing from the spirit and scope of the illustrative examplesdescribed.

The preprocessor 120 preprocesses the sensing data. For example, thepreprocessor 120 removes noise from the sensing data. In an example, thepreprocessor 120 downsamples the sensing data. For example, when avoltage of the battery is sensed at each time interval of 1 second for150 minutes, the receiver 110 sequentially receives 9,000 pieces ofvoltage data. The preprocessor 120 downsamples the 9,000 pieces ofvoltage data at a preset downsampling rate (for example, per minute). Inthis example, 150 pieces of voltage data remain after the downsampling.The removing and the downsampling described above are merely examples ofthe preprocessing, and the preprocessing performed by the preprocessor120 is not limited to those described above.

The selector 130 acquires feature information of the preprocessedsensing data, and determines, based on the feature information, whetherthe preprocessed sensing data is to be selected. For example, theselector 130 determines, based on the feature information, whether thepreprocessed sensing data is suitable for estimation a state of thebattery or whether the preprocessed sensing data reliable.

In an example, the selector 130 verifies a data length of thepreprocessed sensing data, and determines, based on the data length,whether the preprocessed sensing data is to be selected. When the datalength is greater than or equal to a thresholdlength, the selector 130selects the preprocessed sensing data. When the data length is less thanthe threshold length, the selector 130 does not select the preprocessedsensing data. For example, when a number of pieces of sensing dataremaining after the sensing data is downsampled is greater than or equalto a number of pieces of sensing data (for example, 130 pieces ofsensing data), the selector 130 selects the preprocessed sensing data.In an example, when the number of pieces of sensing data remaining afterthe sensing data is downsampled is less than the predetermined number ofpieces of sensing data, the selector 130 does not select thepreprocessed sensing data. The data length will be further describedwith reference to FIG. 2 below.

In another example, the selector 130 determines statistical informationof the preprocessed sensing data. The selector 130 determines whetherthe preprocessed sensing data is to be selected based on the statisticalinformation. The statistical information includes, for example, avariance, and other statistical information sensors may be used withoutdeparting from the spirit and scope of the illustrative examplesdescribed. The statistical information will be further described withreference to FIGS. 3 and 4 below.

The state estimator 140 determines state information of the battery,based on at least one of the preprocessed sensing data or previous stateinformation of the battery, according to a determination result of theselector 130. The state information includes, for example, a capacity orlife information (for example, a state of health (SOH)) of a battery.The state information is not limited to those described above, and otherstate information, such as, state of charge (SOC) may be used withoutdeparting from the spirit and scope of the illustrative examplesdescribed.

When the selector 130 selects the preprocessed sensing data, the stateestimator 140 determines the state information based on the preprocessedsensing data. For example, the state estimator 140 determines, using abattery model, determines the state information based on thepreprocessed sensing data. The battery model is a model configured tooutput state information in response to an input of the sensing data.For example, the battery model is configured to output an SOH of abattery in response to an input of sensing data (or preprocessed sensingdata) of the battery. The battery model includes, for example, anelectrochemical model, or a model that is based on a neural network (forexample, a recurrent neural network (RNN)). The battery model is notlimited to those described above, and other battery models may be usedwithout departing from the spirit and scope of the illustrative examplesdescribed.

The battery model is stored in a memory (not shown) in the battery stateestimation apparatus 100. At least one parameter applied to the batterymodel is also stored in the memory.

When the selector 130 does not select the preprocessed sensing data, thestate estimator 140 determines the state information based on theprevious state information. An example of an operation of the stateestimator 140 when the selector 130 does not select the preprocessedsensing data will be described with reference to FIGS. 5 and 6 below.

The battery state estimation apparatus 100 does not perform an operationbased on sensing data that is unsuitable for state estimation, and thusit is possible to save computing resources. Also, the battery stateestimation apparatus 100 performs an operation based on sensing datasuitable for the state estimation, to estimate the state of the battery,and thus it is possible to increase an accuracy of estimation of acurrent state of the battery.

FIG. 2 illustrates an example of a distribution of estimation errors foreach data length.

FIG. 2 is a diagram 200 illustrating the distribution of the estimationerrors.

In the graph 200, an x-axis represents an estimation error. Theestimation error is a difference between a real capacity of a batteryand an estimated capacity of the battery. An increase in the estimationerror indicates a low accuracy for estimating the capacity of thebattery. In FIG. 2, a value of “3” indicates an estimation error ofabout 5%.

In FIG. 2, a y-axis represents a charge cycle length. The charge cyclelength is a charge duration. A quantity of sensing data to be generatedincreases as the length of the charge cycle increases, and accordinglythe charge cycle length is a data length of the sensing data.

In an example, when the charge cycle length is less than a length x,estimation errors are distributed in a region with values that aregreater than “0” and less than or equal to “6.” As the data lengthdecreases, there is an increase in a range in which the estimationerrors are distributed. Thus, when a current state of the battery isestimated based on sensing data corresponding to a short charge cyclelength (that is, a short data length), it is difficult to guarantee thatan estimation error of the estimated current state is low. For example,when a current capacity of a battery is estimated based on sensing datacorresponding to a charge cycle length of 100 minutes (min), anestimation error of the estimated current capacity corresponds to avalue less than “1” or a value greater than “3” in the graph 200. It isdifficult to guarantee that the estimation error of the estimatedcurrent state corresponds to the value less than “1.” Thus, sensing datawith a short data length has a low reliability for state estimation andis unsuitable for the state estimation.

In another example, when the charge cycle length is greater than orequal to the length x, estimation errors are mainly distributed in aregion with values less than “1.” In other words, as the data lengthincreases, a range in which estimation errors are distributed decreasesand an estimation accuracy increases. Thus, when a current state of thebattery is estimated based on sensing data corresponding to a longcharge cycle length (that is, a long data length), an estimation errorof the estimated current state is low. For example, when a currentcapacity of a battery is estimated based on sensing data correspondingto a charge cycle length of 400 min, a probability that an estimationerror of the estimated current capacity has a value less than “1” in thegraph 200 is high. Thus, sensing data with a long data length has a highreliability for state estimation and is suitable for the stateestimation.

A data length of sensing data (or preprocessed sensing data) isassociated with a reliability and estimation accuracy of the sensingdata (or the preprocessed sensing data). Thus, the selector 130 selectsthe sensing data (or the preprocessed sensing data) based on the datalength.

FIGS. 3 and 4 illustrate examples of statistical information of sensingdata.

FIG. 3 illustrates voltage data of battery 1. The selector 130determines statistical information (for example, a variance of adeviation) of the voltage data of the battery 1. For example, theselector 130 calculates a deviation between sample values of the voltagedata of the battery 1, and determines the statistical information basedon the deviation. In FIG. 3, the selector 130 calculates a differenceΔV_(1_1) between a first sample value and a second sample value, and adifference ΔV_(1_2) between the second sample value and a third samplevalue. Similarly, the selector 130 calculates differences ΔV_(1_3) andΔV_(1_4). The selector 130 determines a variance of the differencesΔV_(1_1) through ΔV_(1_4) as the statistical information. Thestatistical information of the voltage data of the battery 1 is denotedby var1.

In FIG. 3, the difference ΔV_(1_1) corresponds to a largest deviation inthe voltage data of the battery 1. The difference ΔV_(1_2) is less thanthe difference ΔV_(1_1), and the difference ΔV_(1_3) is less than thedifference ΔV_(1_2). When the deviation between the sample values of thevoltage data of the battery 1 gradually decreases, a relatively smallamount of the statistical information var1 is obtained, and the selector130 selects the voltage data of the battery 1. In other words, theselector 130 determines the voltage data of the battery 1 to have a highreliability or to be suitable for estimation of a state of the battery1.

FIG. 4 illustrates voltage data of a battery 2. The voltage data of thebattery 2 includes noise (not shown). In FIG. 4, the selector 130calculates differences ΔV_(2_1), ΔV_(2_2), ΔV_(2_3), and ΔV_(2_4), anddetermines a variance of the differences ΔV_(2_1) through ΔV_(2_4) asstatistical information of the voltage data of the battery 2. The stateinformation of the voltage data of the battery 2 is denoted by var2.

In FIG. 4, a deviation between sample values of the voltage data of thebattery 2 does not gradually decrease due to the noise. The deviationbetween the sample values irregularly changes, for example, decreasesand then increases, instead of gradually decreasing. For example, thedifference ΔV_(2_2) is less than the difference ΔV_(2_1) and thedifference ΔV_(2_3) is greater than the difference ΔV_(2_2) due to thenoise. When the deviation between the sample values of the voltage dataof the battery 2 does not gradually decrease, a relatively large amountof the statistical information var2 is obtained, and the selector 130does not select the voltage data of the battery 2. In other words, theselector 130 determines the voltage data of the battery 2 to have a lowreliability or to be unsuitable for estimation of a state of the battery2.

Noise has an influence on an accuracy of estimation of a state of abattery. Due to the noise, estimation error occurs. Accordingly, theselector 130 does not select sensing data that is expected to createestimation error in estimating the state of the battery.

The voltage data of FIG. 3 and the voltage data of FIG. 4 are merelyexamples of sensing data, and the sensing data is not limited to thevoltage data of FIG. 3 or 4. The above description of FIGS. 3 and 4 arealso applicable to data (for example, current data or temperature data)associated with the other physical quantities of a battery.

FIGS. 5 and 6 illustrate examples of estimation of a state of a batterywhen sensing data is unsuitable for the estimation of the state.

FIG. 5 illustrates a capacity of a battery over time and a reliabilityof sensing data.

The reliability of the sensing data is digitized. For example, theselector 130 calculates or determines the reliability based on featureinformation of the sensing data. In an example, the selector 130determines the reliability based on a data length or statisticalinformation.

The selector 130 calculates the reliability based on both the datalength and the statistical information. For example, the selector 130determines, based on a lookup table, a first score corresponding to thedata length and a second score corresponding to the statisticalinformation, and determines a sum of the first score and the secondscore as the reliability. In an example, a weight is applied to thefirst score and/or the second score. The reliability is also referred toas a suitability.

A reliability of sensing data at a time t₁ is greater than a referencevalue. Accordingly, the selector 130 determines that the sensing data atthe time t₁ is suitable for state estimation, and the state estimator140 estimates a capacity 510 based on the sensing data at the time t₁.

A reliability of sensing data at a time t₂ is greater than the referencevalue, and accordingly the selector 130 selects the sensing data at thetime t₂. The state estimator 140 estimates a capacity 520 based on thesensing data at the time t₂. Similarly, the state estimator 140estimates a capacity 530 based on sensing data at a time t₃.

A reliability of sensing data at a time t₄ is less than the referencevalue. For example, when sensing data includes noise, for example, asensor error, or when a data length is not long enough because a batteryis charged for a relatively short period of time, a reliability of thesensing data is less than the reference value. The selector 130determines that the sensing data at the time t₄ is unsuitable for stateestimation. The state estimator 140 estimates a capacity 540 based onthe capacities 510, 520 and/or 530 that are already estimated, insteadof using the sensing data at the time t₄. Thus, the state estimator 140does not need to perform an operation based on the sensing data at thetime t₄, to prevent unnecessary use of resources.

In an example, referring to FIG. 5, the state estimator 140 determinesthe capacity 540 to be equal to the capacity 530. In other words, thestate estimator 140 determines the capacity 540 as the capacity 530. Inan example, the state estimator 140 applies an extrapolation to thecapacities 510, 520 and/or 530, to estimate the capacity 540. The stateestimator 140 determines an average value of the capacities 510 through530 as the capacity 540. Various extrapolation schemes are applicable toestimation of the capacity 540.

In another example, referring to FIG. 6, the state estimator 140estimates the capacity 540 based on a reduction in an efficiency due toan increase in a number of cycles. In an example, a cumulative number ofcharge and discharge cycles at a time t₃ is assumed as n₁, and acumulative number of charge and discharge cycles at a time t₄ is assumedas n₂. In this example, the state estimator 140 determines that theefficiency is reduced by a value obtained by subtracting n₁ from n₂ andapplies the reduced efficiency to the capacity 530, to estimate thecapacity 540. When the reduced efficiency is denoted by cap₁, thecapacity 540 is determined as a value obtained by subtracting cap, fromthe capacity 530.

When a reliability of sensing data at the time t₄ is less than thereference value, the state estimator 140 skips estimation of a state ofa battery at the time t₄. In other words, when the reliability of thesensing data at the time t₄ is less than the reference value, thecapacity 540 at the time t₄ is not estimated.

FIG. 7 illustrates an example of a battery state estimation result.

FIG. 7 illustrates a capacity of a battery over time.

At times t₄, t₈ and t₉ in which reliabilities are less than a referencevalue, the capacity of the battery is not estimated.

For example, when a request for estimated capacities of the battery isreceived from a server, estimated capacities of the battery at the timest₄, t₈ and t₉ are required. In an example, the state estimator 140applies an interpolation to the estimated capacities, to estimate thecapacities at the times t₄, t₈ and t₉.

FIGS. 8 and 9 illustrate examples of a battery management system (BMS)with a master-slave structure.

Referring to FIGS. 8 and 9, a BMS includes a slave manager 810 or 910,and a master manager 820 or 920. In another example, the BMS includes aplurality of slave managers 810 or 910.

The slave manager 810 or 910 manages and/or controls each of batterycells included in a battery pack 830 or 930. The master manager 820 or920 controls the slave manager 810 or 910.

The slave manager 810 or 910 and the master manager 820 or 920 performfunctions or operations of the above-described battery state estimationapparatus 100 of FIG. 1. The examples of FIGS. 8 and 9 are describedbelow.

In the example of FIG. 8, the slave manager 810 includes a receiver 110,a preprocessor 120 and a selector 130, and the master manager 820includes a state estimator 140.

In FIG. 8, the slave manager 810 receives sensing data of each of thebattery cells from a sensor, preprocesses the sensing data, anddetermines whether the preprocessed sensing data is to be selected. Whenthe preprocessed sensing data is selected, the slave manager 810transmits the preprocessed sensing data to the master manager 820.

When the preprocessed sensing data is received from the slave manager810, the master manager 820 determines state information of each of thebattery cells based on the preprocessed sensing data. When thepreprocessed sensing data is not received from the slave manager 810,the master manager 820 determines state information of each of thebattery cells based on previous state information. The master manager820 determines state information of the battery pack 830 based on thestate information of each of the battery cells.

Depending on examples, when the preprocessed sensing data is selected,the slave manager 810 determines state information of each of thebattery cells based on the preprocessed sensing data, instead oftransmitting the preprocessed sensing data to the master manager 820.The master manager 820 receives the state information of each of thebattery cells from the slave manager 810 and determines stateinformation of the battery pack 830 based on the received stateinformation.

In the example of FIG. 9, the slave manager 910 includes a receiver 110,and the master manager 920 includes a preprocessor 120, a selector 130and a state estimator 140.

The slave manager 910 receives sensing data of each of battery cellsfrom a sensor, and transmits the sensing data to the master manager 920.The master manager 920 preprocesses the sensing data, and determineswhether the preprocessed sensing data is to be selected. When thepreprocessed sensing data is selected, the master manager 920 determinesstate information of each of the battery cells based on the preprocessedsensing data. The master manager 920 determines state information of thebattery pack 930 based on the state information of each of the batterycells.

Depending on examples, the slave manager 910 preprocesses sensing dataof each of the battery cells and transmits the preprocessed sensing datato the master manager 920. The master manager 920 determines whether thepreprocessed sensing data is to be selected. When the preprocessedsensing data is selected, the master manager 920 determines stateinformation of each of the battery cells based on the preprocessedsensing data. Also, the master manager 920 determines state informationof the battery pack 930 based on the state information of each of thebattery cells.

The above description of FIGS. 1 through 7 is also applicable to theexamples of FIGS. 8 and 9, and thus is not repeated herein.

FIG. 10 is a diagram illustrating an example of a battery stateestimation method. The operations in FIG. 10 may be performed in thesequence and manner as shown, although the order of some operations maybe changed or some of the operations omitted without departing from thespirit and scope of the illustrative examples described. Many of theoperations shown in FIG. 10 may be performed in parallel orconcurrently. One or more blocks of FIG. 10, and combinations of theblocks, can be implemented by special purpose hardware-based computerthat perform the specified functions, or combinations of special purposehardware and computer instructions. In addition to the description ofFIG. 10 below, the above descriptions of FIGS. 1-9 is also applicable toFIG. 10, and are incorporated herein by reference. Thus, the abovedescription may not be repeated here.

The battery state estimation method of FIG. 10 is performed by, forexample, a battery state estimation apparatus.

Referring to FIG. 10, in 1010, the battery state estimation apparatusreceives sensing data of a battery.

In 1020, the battery state estimation apparatus preprocesses the sensingdata.

In 1030, the battery state estimation apparatus determines whether thepreprocessed sensing data is to be selected, based on featureinformation of the preprocessed sensing data. In an example, when alength of the preprocessed sensing data is greater than or equal to alength, the battery state estimation apparatus selects the preprocessedsensing data. In another example, the battery state estimation apparatuscalculates a deviation between each of sample values of the preprocessedsensing data and a previous sample value of each of the sample values.The battery state estimation apparatus selects the preprocessed sensingdata based on a variance of the calculated deviation.

In 1040, the battery state estimation apparatus determines stateinformation of the battery based on the preprocessed sensing data whenthe preprocessed sensing data is selected.

In 1050, the battery state estimation apparatus determines the stateinformation based on previous state information of the battery when thepreprocessed sensing data is not selected.

FIGS. 11 and 12 illustrate examples of vehicles using the battery stateestimation apparatus.

Referring to FIG. 11, a vehicle 1110 uses electric energy forpropulsion. The vehicle 1110 includes, for example, an electric vehicle(EV), a plug-in hybrid electric vehicle (PHEV), or a hybrid electricvehicle (HEV).

The vehicle 1110 includes a battery system 1120.

The battery system 1120 includes a battery pack 1130 and a BMS 1140. InFIG. 11, the BMS 1140 is located outside the battery pack 1130, however,this is merely an example. In another example, the BMS 1140 may beincluded in the battery pack 1130.

The battery pack 1130 includes battery modules 1131, 1132 and 1133. Eachof the battery modules 1131, 1132 and 1133 includes at least one batterycell.

Slave managers 1141, 1142 and 1143 and a master manager 1144 performfunctions or operations of the above-described battery state estimationapparatus 100 of FIG. 1. For example, each of the slave managers 1141,1142 and 1143 corresponds to the slave manager 810 of FIG. 8 or theslave manager 910 of FIG. 9, and the master manager 1144 corresponds tothe master manager 820 of FIG. 8 or the master manager 920 of FIG. 9.The master manager 1144 determines state information of the battery pack1130.

The master manager 1144 displays the state information of the batterypack 1130 on a display in the vehicle 1110 using a vehicle controller(not shown). For example, referring to FIG. 12, state information 1210of a battery pack is displayed on a display. In an example, the displaymay be a physical structure that includes one or more hardwarecomponents that provide the ability to render a user interface and/orreceive user input. The display can encompass any combination of displayregion, gesture capture region, a touch sensitive display, and/or aconfigurable area. In an example, the display can be embedded in thebattery state estimation apparatus. In an example, the display is anexternal peripheral device that may be attached to and detached from thebattery state estimation apparatus. The display may be a single-screenor a multi-screen display. A single physical screen can include multipledisplays that are managed as separate logical displays permittingdifferent content to be displayed on separate displays although part ofthe same physical screen. In an example, the display is a head-updisplay (HUD), a vehicular infotainment system, a dashboard in avehicle, or a screen in the vehicle that used augmented reality. Thedisplay may also be implemented as an eye glass display (EGD), whichincludes one-eyed glass or two-eyed glasses.

Depending on examples, the master manager 1144 acoustically outputs thestate information of the battery pack 1130 using the vehicle controller.

FIGS. 1 through 10 is also applicable to the example of FIG. 11, andthus is not repeated herein.

In an example, the battery state estimation apparatus or the BMS isincluded in a large capacity battery system, for example, an energystorage system (ESS).

In an example, the battery state estimation apparatus or the BMS isincluded in a device management system or an electronic device thatincludes a rechargeable battery, such as, for example, an intelligentagent, a mobile phone, a cellular phone, a smart phone, a wearable smartdevice (such as, for example, a ring, a watch, a pair of glasses,glasses-type device, a bracelet, an ankle bracket, a belt, a necklace,an earring, a headband, a helmet, a device embedded in the cloths), apersonal computer (PC), a laptop, a notebook, a subnotebook, a netbook,or an ultra-mobile PC (UMPC), a tablet personal computer (tablet), aphablet, a mobile internet device (MID), a personal digital assistant(PDA), an enterprise digital assistant (EDA), a digital camera, adigital video camera, a portable game console, an MP3 player, aportable/personal multimedia player (PMP), a handheld e-book, an ultramobile personal computer (UMPC), a portable lab-top PC, a globalpositioning system (GPS) navigation, a personal navigation device orportable navigation device (PND), a handheld game console, an e-book,and devices such as a high definition television (HDTV), an optical discplayer, a DVD player, a Blue-ray player, a setup box, robot cleaners, ahome appliance, a smart appliance, content players, communicationsystems, image processing systems, graphics processing systems, otherconsumer electronics/information technology (CE/IT) device, or any otherdevice capable of wireless communication or network communicationconsistent with that disclosed herein or various other Internet ofThings (IoT) devices that are controlled through a network.

The electronic devices may also be implemented as a wearable device,which is worn on a body of a user. In one example, a wearable device maybe self-mountable on the body of the user, such as, for example, a ring,a watch, a pair of glasses, glasses-type device, a bracelet, an anklebracket, a belt, a band, an anklet, a belt necklace, an earring, aheadband, a helmet, a device embedded in the cloths, or as an eye glassdisplay (EGD), which includes one-eyed glass or two-eyed glasses.

The battery state estimation apparatus 100, receiver 110, preprocessor120, selector 130, state estimator 140, slave manager 1141, 1142, 1143810 or 910, master manager 820, 1144, or 920, BMS 1140, and otherapparatuses, units, modules, devices, components are implemented byhardware components. Examples of hardware components that may be used toperform the operations described in this application where appropriateinclude controllers, sensors, generators, drivers, memories,comparators, arithmetic logic units, adders, subtractors, multipliers,dividers, integrators, and any other electronic components configured toperform the operations described in this application. In other examples,one or more of the hardware components that perform the operationsdescribed in this application are implemented by computing hardware, forexample, by one or more processors or computers. A processor or computermay be implemented by one or more processing elements, such as an arrayof logic gates, a controller and an arithmetic logic unit, a digitalsignal processor, a microcomputer, a programmable logic controller, afield-programmable gate array, a programmable logic array, amicroprocessor, or any other device or combination of devices that isconfigured to respond to and execute instructions in a defined manner toachieve a desired result. In one example, a processor or computerincludes, or is connected to, one or more memories storing instructionsor software that are executed by the processor or computer. Hardwarecomponents implemented by a processor or computer may executeinstructions or software, such as an operating system (OS) and one ormore software applications that run on the OS, to perform the operationsdescribed in this application. The hardware components may also access,manipulate, process, create, and store data in response to execution ofthe instructions or software. For simplicity, the singular term“processor” or “computer” may be used in the description of the examplesdescribed in this application, but in other examples multiple processorsor computers may be used, or a processor or computer may includemultiple processing elements, or multiple types of processing elements,or both. For example, a single hardware component or two or morehardware components may be implemented by a single processor, or two ormore processors, or a processor and a controller. One or more hardwarecomponents may be implemented by one or more processors, or a processorand a controller, and one or more other hardware components may beimplemented by one or more other processors, or another processor andanother controller. One or more processors, or a processor and acontroller, may implement a single hardware component, or two or morehardware components. A hardware component may have any one or more ofdifferent processing configurations, examples of which include a singleprocessor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIG. 10 that perform the operations describedin this application are performed by computing hardware, for example, byone or more processors or computers, implemented as described aboveexecuting instructions or software to perform the operations describedin this application that are performed by the methods. For example, asingle operation or two or more operations may be performed by a singleprocessor, or two or more processors, or a processor and a controller.One or more operations may be performed by one or more processors, or aprocessor and a controller, and one or more other operations may beperformed by one or more other processors, or another processor andanother controller. One or more processors, or a processor and acontroller, may perform a single operation, or two or more operations.

Instructions or software to control a processor or computer to implementthe hardware components and perform the methods as described above arewritten as computer programs, code segments, instructions or anycombination thereof, for individually or collectively instructing orconfiguring the processor or computer to operate as a machine orspecial-purpose computer to perform the operations performed by thehardware components and the methods as described above. In one example,the instructions or software include machine code that is directlyexecuted by the processor or computer, such as machine code produced bya compiler. In another example, the instructions or software includehigher-level code that is executed by the processor or computer using aninterpreter. Programmers of ordinary skill in the art can readily writethe instructions or software based on the block diagrams and the flowcharts illustrated in the drawings and the corresponding descriptions inthe specification, which disclose algorithms for performing theoperations performed by the hardware components and the methods asdescribed above.

The instructions or software to control a processor or computer toimplement the hardware components and perform the methods as describedabove, and any associated data, data files, and data structures, arerecorded, stored, or fixed in or on one or more non-transitorycomputer-readable storage media. Examples of a non-transitorycomputer-readable storage medium include read-only memory (ROM),random-access programmable read only memory (PROM), electricallyerasable programmable read-only memory (EEPROM), random-access memory(RAM), dynamic random access memory (DRAM), static random access memory(SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs,CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs,BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-ray or optical disk storage,hard disk drive (HDD), solid state drive (SSD), flash memory, a cardtype memory such as multimedia card micro or a card (for example, securedigital (SD) or extreme digital (XD)), magnetic tapes, floppy disks,magneto-optical data storage devices, optical data storage devices, harddisks, solid-state disks, and any other device that is configured tostore the instructions or software and any associated data, data files,and data structures in a non-transitory manner and providing theinstructions or software and any associated data, data files, and datastructures to a processor or computer so that the processor or computercan execute the instructions.

While this disclosure includes specific examples, it will be apparent toone of ordinary skill in the art that various changes in form anddetails may be made in these examples without departing from the spiritand scope of the claims and their equivalents. The examples describedherein are to be considered in a descriptive sense only, and not forpurposes of limitation. Descriptions of features or aspects in eachexample are to be considered as being applicable to similar features oraspects in other examples. Suitable results may be achieved if thedescribed techniques are performed in a different order, and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner, and/or replaced or supplemented by othercomponents or their equivalents. Therefore, the scope of the disclosureis defined not by the detailed description, but by the claims and theirequivalents, and all variations within the scope of the claims and theirequivalents are to be construed as being included in the disclosure.

What is claimed is:
 1. A battery state estimation method comprising:receiving sensing data of a battery; preprocessing the sensing data;calculating a difference value between each of sample values of thepreprocessed sensing data and an adjacent sample value of the each ofthe sample values; determining statistic information of the calculateddifference values; determining whether the preprocessed sensing data issuitable for battery state estimation based on the determined statisticinformation; determining state information of the battery based on thepreprocessed sensing data in response to the preprocessed sensing databeing suitable for the battery state estimation; and determining thestate information based on a previous state information of the batteryin response to the preprocessed sensing data not being suitable for thebattery state estimation.
 2. The battery state estimation method ofclaim 1, wherein the statistic information comprises a variance of thecalculated difference values.
 3. The battery state estimation method ofclaim 1, wherein the preprocessing of the sensing data comprises:filtering the sensing data; and downsampling the filtered sensing data.4. A non-transitory computer-readable storage medium storinginstructions, that when executed by a processor, causes the processor toperform the battery state estimation method of claim
 1. 5. A batterystate estimation apparatus comprising: a memory; and a controllercoupled to the memory, wherein the controller is configured to receivesensing data of a battery, preprocess the sensing data, calculate adifference value between each of sample values of the preprocessedsensing data and an adjacent sample value of the each of the samplevalues, determine statistic information of the calculated differencevalues, determine whether the preprocessed sensing data is suitable forbattery state estimation based on the determined statistic information,determine state information of the battery based on the preprocessedsensing data in response to the preprocessed sensing data being suitablefor the battery state estimation, and determine the state informationbased on a previous state information of the battery in response to thepreprocessed sensing data not being suitable for the battery stateestimation.
 6. The battery state estimation apparatus of claim 5,wherein the statistic information comprises a variance of the calculateddifference values.
 7. The battery state estimation apparatus of claim 5,wherein the controller is further configured to: filter the sensingdata; and downsample the filtered sensing data.